Vertical search

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A vertical search engine, as distinct from a general web search engine, focuses on a specific segment of online content. They are also called specialty or topical search engines. The vertical content area may be based on topicality, media type, or genre of content. Common verticals include shopping, the automotive industry, legal information, medical information, scholarly literature, and travel. Examples of vertical search engines include Trulia.com and Yelp. In contrast to general Web search engines, which attempt to index large portions of the World Wide Web using a web crawler, vertical search engines typically use a focused crawler that attempts to index only Web pages that are relevant to a pre-defined topic or set of topics.

Some vertical search sites focus on individual verticals, while other sites include multiple vertical searches within one search engine.

Vertical search offers several potential benefits over general search engines:

  • Greater precision due to limited scope,
  • Leverage domain knowledge including taxonomies and ontologies,
  • Support of specific unique user tasks.

Vertical search can be viewed as similar to enterprise search where the domain of focus is the enterprise, such as a company, government or other organization. In 2013, consumer price comparison websites with integrated vertical search engines such as FindTheBest drew large rounds of venture capital funding, indicating a growth trend for these applications of vertical search technology.[1][2]

Domain-specific search

Domain-specific verticals focus on a specific topic. John Battelle describes this in his book, The Search:

Domain-specific search solutions focus on one area of knowledge, creating customized search experiences, that because of the domain's limited corpus and clear relationships between concepts, provide extremely relevant results for searchers.[3]

In the domain-specific setting, it is possible to combine the tf-idf approach implemented via an inverse index with semantic approaches of semantic headers and semantic skeletons. Instead of most frequent keywords, a set of entities is extracted from a portion of text to be matched against a potential question. This allows much more flexibility due to real time reasoning capabilities while matching questions and answers in the form of semantic headers. [4]

References

  1. ↑ Rao, Leena. "Data-Driven Comparison Shopping Platform FindTheBest Raises $11M From New World, Kleiner Perkins And Others". TechCrunch. Retrieved 27 May 2013. 
  2. ↑ HO, VICTORIA. "Asian Price Comparison Site Save 22 Gets Angel Round Of “Mid Six Figures”". Retrieved 27 May 2013. 
  3. ↑ Battelle, John (2005). The Search: How Google and its Rivals Rewrote the Rules of Business and Transformed Our Culture. New York: Portfolio. 
  4. ↑ Galitsky, Boris (2006). "Building a Repository of Background Knowledge Using Semantic Skeletons". AAAI Spring Symposium: Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering (AAAI). 
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